Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations1408
Missing cells9856
Missing cells (%)21.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory993.7 B

Variable types

Categorical14
Unsupported7
Text2
Numeric9
Boolean1

Alerts

intentType has constant value "science" Constant
obs_collection has constant value "TESS" Constant
provenance_name has constant value "SPOC" Constant
instrument_name has constant value "Photometer" Constant
project has constant value "TESS" Constant
filters has constant value "TESS" Constant
wavelength_region has constant value "Optical" Constant
target_name has constant value "TESS FFI" Constant
dataproduct_type has constant value "image" Constant
proposal_pi has constant value "Ricker, George" Constant
calib_level has constant value "3" Constant
em_min has constant value "600.0" Constant
em_max has constant value "1000.0" Constant
dataRights has constant value "PUBLIC" Constant
mtFlag has constant value "False" Constant
objID is highly overall correlated with obsid and 5 other fieldsHigh correlation
obsid is highly overall correlated with objID and 5 other fieldsHigh correlation
sequence_number is highly overall correlated with objID and 5 other fieldsHigh correlation
t_exptime is highly overall correlated with objID and 5 other fieldsHigh correlation
t_max is highly overall correlated with objID and 5 other fieldsHigh correlation
t_min is highly overall correlated with objID and 5 other fieldsHigh correlation
t_obs_release is highly overall correlated with objID and 5 other fieldsHigh correlation
target_classification has 1408 (100.0%) missing values Missing
obs_title has 1408 (100.0%) missing values Missing
proposal_id has 1408 (100.0%) missing values Missing
proposal_type has 1408 (100.0%) missing values Missing
jpegURL has 1408 (100.0%) missing values Missing
dataURL has 1408 (100.0%) missing values Missing
srcDen has 1408 (100.0%) missing values Missing
obs_id has unique values Unique
s_ra has unique values Unique
s_dec has unique values Unique
t_min has unique values Unique
t_max has unique values Unique
s_region has unique values Unique
obsid has unique values Unique
objID has unique values Unique
target_classification is an unsupported type, check if it needs cleaning or further analysis Unsupported
obs_title is an unsupported type, check if it needs cleaning or further analysis Unsupported
proposal_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
proposal_type is an unsupported type, check if it needs cleaning or further analysis Unsupported
jpegURL is an unsupported type, check if it needs cleaning or further analysis Unsupported
dataURL is an unsupported type, check if it needs cleaning or further analysis Unsupported
srcDen is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-05-25 14:33:53.502514
Analysis finished2025-05-25 14:34:04.456331
Duration10.95 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

intentType
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.1 KiB
science
1408 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters9856
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowscience
2nd rowscience
3rd rowscience
4th rowscience
5th rowscience

Common Values

ValueCountFrequency (%)
science 1408
100.0%

Length

2025-05-25T15:34:04.580971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:04.671729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
science 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
c 2816
28.6%
e 2816
28.6%
s 1408
14.3%
i 1408
14.3%
n 1408
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 2816
28.6%
e 2816
28.6%
s 1408
14.3%
i 1408
14.3%
n 1408
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 2816
28.6%
e 2816
28.6%
s 1408
14.3%
i 1408
14.3%
n 1408
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 2816
28.6%
e 2816
28.6%
s 1408
14.3%
i 1408
14.3%
n 1408
14.3%

obs_collection
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
TESS
1408 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5632
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTESS
2nd rowTESS
3rd rowTESS
4th rowTESS
5th rowTESS

Common Values

ValueCountFrequency (%)
TESS 1408
100.0%

Length

2025-05-25T15:34:04.755504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:04.820370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tess 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

provenance_name
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
SPOC
1408 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5632
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSPOC
2nd rowSPOC
3rd rowSPOC
4th rowSPOC
5th rowSPOC

Common Values

ValueCountFrequency (%)
SPOC 1408
100.0%

Length

2025-05-25T15:34:04.911124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:04.991910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
spoc 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
S 1408
25.0%
P 1408
25.0%
O 1408
25.0%
C 1408
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1408
25.0%
P 1408
25.0%
O 1408
25.0%
C 1408
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1408
25.0%
P 1408
25.0%
O 1408
25.0%
C 1408
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1408
25.0%
P 1408
25.0%
O 1408
25.0%
C 1408
25.0%

instrument_name
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size81.3 KiB
Photometer
1408 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters14080
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhotometer
2nd rowPhotometer
3rd rowPhotometer
4th rowPhotometer
5th rowPhotometer

Common Values

ValueCountFrequency (%)
Photometer 1408
100.0%

Length

2025-05-25T15:34:05.087616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:05.174385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
photometer 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
o 2816
20.0%
e 2816
20.0%
t 2816
20.0%
h 1408
10.0%
P 1408
10.0%
m 1408
10.0%
r 1408
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2816
20.0%
e 2816
20.0%
t 2816
20.0%
h 1408
10.0%
P 1408
10.0%
m 1408
10.0%
r 1408
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2816
20.0%
e 2816
20.0%
t 2816
20.0%
h 1408
10.0%
P 1408
10.0%
m 1408
10.0%
r 1408
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2816
20.0%
e 2816
20.0%
t 2816
20.0%
h 1408
10.0%
P 1408
10.0%
m 1408
10.0%
r 1408
10.0%

project
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
TESS
1408 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5632
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTESS
2nd rowTESS
3rd rowTESS
4th rowTESS
5th rowTESS

Common Values

ValueCountFrequency (%)
TESS 1408
100.0%

Length

2025-05-25T15:34:05.273137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:05.344927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tess 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

filters
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.0 KiB
TESS
1408 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5632
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTESS
2nd rowTESS
3rd rowTESS
4th rowTESS
5th rowTESS

Common Values

ValueCountFrequency (%)
TESS 1408
100.0%

Length

2025-05-25T15:34:05.428732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:05.507494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tess 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5632
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2816
50.0%
T 1408
25.0%
E 1408
25.0%

wavelength_region
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.1 KiB
Optical
1408 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters9856
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOptical
2nd rowOptical
3rd rowOptical
4th rowOptical
5th rowOptical

Common Values

ValueCountFrequency (%)
Optical 1408
100.0%

Length

2025-05-25T15:34:05.615207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:05.686055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
optical 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
O 1408
14.3%
p 1408
14.3%
t 1408
14.3%
i 1408
14.3%
c 1408
14.3%
a 1408
14.3%
l 1408
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1408
14.3%
p 1408
14.3%
t 1408
14.3%
i 1408
14.3%
c 1408
14.3%
a 1408
14.3%
l 1408
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1408
14.3%
p 1408
14.3%
t 1408
14.3%
i 1408
14.3%
c 1408
14.3%
a 1408
14.3%
l 1408
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1408
14.3%
p 1408
14.3%
t 1408
14.3%
i 1408
14.3%
c 1408
14.3%
a 1408
14.3%
l 1408
14.3%

target_name
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.5 KiB
TESS FFI
1408 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters11264
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTESS FFI
2nd rowTESS FFI
3rd rowTESS FFI
4th rowTESS FFI
5th rowTESS FFI

Common Values

ValueCountFrequency (%)
TESS FFI 1408
100.0%

Length

2025-05-25T15:34:05.813675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:06.048050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tess 1408
50.0%
ffi 1408
50.0%

Most occurring characters

ValueCountFrequency (%)
F 2816
25.0%
S 2816
25.0%
E 1408
12.5%
T 1408
12.5%
1408
12.5%
I 1408
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11264
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 2816
25.0%
S 2816
25.0%
E 1408
12.5%
T 1408
12.5%
1408
12.5%
I 1408
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11264
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 2816
25.0%
S 2816
25.0%
E 1408
12.5%
T 1408
12.5%
1408
12.5%
I 1408
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11264
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 2816
25.0%
S 2816
25.0%
E 1408
12.5%
T 1408
12.5%
1408
12.5%
I 1408
12.5%

target_classification
Unsupported

Missing  Rejected  Unsupported 

Missing1408
Missing (%)100.0%
Memory size11.1 KiB

obs_id
Text

Unique 

Distinct1408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size86.8 KiB
2025-05-25T15:34:06.296385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters19712
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1408 ?
Unique (%)100.0%

Sample

1st rowtess-s0086-1-1
2nd rowtess-s0086-1-2
3rd rowtess-s0086-1-3
4th rowtess-s0086-1-4
5th rowtess-s0086-2-1
ValueCountFrequency (%)
tess-s0088-1-3 1
 
0.1%
tess-s0088-1-4 1
 
0.1%
tess-s0088-2-1 1
 
0.1%
tess-s0088-2-2 1
 
0.1%
tess-s0088-2-3 1
 
0.1%
tess-s0088-2-4 1
 
0.1%
tess-s0088-3-1 1
 
0.1%
tess-s0088-3-2 1
 
0.1%
tess-s0060-4-4 1
 
0.1%
tess-s0085-4-4 1
 
0.1%
Other values (1398) 1398
99.3%
2025-05-25T15:34:06.737211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 4224
21.4%
s 4224
21.4%
0 3088
15.7%
t 1408
 
7.1%
e 1408
 
7.1%
1 1008
 
5.1%
4 1008
 
5.1%
2 1008
 
5.1%
3 1008
 
5.1%
7 304
 
1.5%
Other values (4) 1024
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 4224
21.4%
s 4224
21.4%
0 3088
15.7%
t 1408
 
7.1%
e 1408
 
7.1%
1 1008
 
5.1%
4 1008
 
5.1%
2 1008
 
5.1%
3 1008
 
5.1%
7 304
 
1.5%
Other values (4) 1024
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 4224
21.4%
s 4224
21.4%
0 3088
15.7%
t 1408
 
7.1%
e 1408
 
7.1%
1 1008
 
5.1%
4 1008
 
5.1%
2 1008
 
5.1%
3 1008
 
5.1%
7 304
 
1.5%
Other values (4) 1024
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 4224
21.4%
s 4224
21.4%
0 3088
15.7%
t 1408
 
7.1%
e 1408
 
7.1%
1 1008
 
5.1%
4 1008
 
5.1%
2 1008
 
5.1%
3 1008
 
5.1%
7 304
 
1.5%
Other values (4) 1024
 
5.2%

s_ra
Real number (ℝ)

Unique 

Distinct1408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.53783
Minimum0.28911547
Maximum359.49146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2025-05-25T15:34:06.867894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.28911547
5-th percentile23.697911
Q190.615334
median171.41525
Q3271.20577
95-th percentile336.51663
Maximum359.49146
Range359.20234
Interquartile range (IQR)180.59044

Descriptive statistics

Standard deviation101.47253
Coefficient of variation (CV)0.56835313
Kurtosis-1.2961401
Mean178.53783
Median Absolute Deviation (MAD)90.218225
Skewness0.056717135
Sum251381.26
Variance10296.675
MonotonicityNot monotonic
2025-05-25T15:34:07.024439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131.2542873 1
 
0.1%
119.2602239 1
 
0.1%
110.609962 1
 
0.1%
68.36052358 1
 
0.1%
81.02428957 1
 
0.1%
106.782678 1
 
0.1%
263.9402795 1
 
0.1%
252.6451194 1
 
0.1%
288.3581057 1
 
0.1%
295.5842127 1
 
0.1%
Other values (1398) 1398
99.3%
ValueCountFrequency (%)
0.2891154663 1
0.1%
0.7066645516 1
0.1%
1.005209948 1
0.1%
1.619925186 1
0.1%
2.057237255 1
0.1%
2.388178814 1
0.1%
3.210835911 1
0.1%
3.852119738 1
0.1%
4.797742411 1
0.1%
5.04531068 1
0.1%
ValueCountFrequency (%)
359.4914597 1
0.1%
359.4354835 1
0.1%
359.242507 1
0.1%
359.0061802 1
0.1%
358.8169531 1
0.1%
358.7326265 1
0.1%
358.1697198 1
0.1%
358.0999891 1
0.1%
357.3322319 1
0.1%
357.2034932 1
0.1%

s_dec
Real number (ℝ)

Unique 

Distinct1408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3497293
Minimum-85.380468
Maximum89.118477
Zeros0
Zeros (%)0.0%
Negative594
Negative (%)42.2%
Memory size11.1 KiB
2025-05-25T15:34:07.168054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-85.380468
5-th percentile-72.167423
Q1-43.141916
median16.348619
Q356.245241
95-th percentile75.002553
Maximum89.118477
Range174.49895
Interquartile range (IQR)99.387157

Descriptive statistics

Standard deviation51.252636
Coefficient of variation (CV)6.9734045
Kurtosis-1.3764095
Mean7.3497293
Median Absolute Deviation (MAD)45.433235
Skewness-0.2186853
Sum10348.419
Variance2626.8327
MonotonicityNot monotonic
2025-05-25T15:34:07.348571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-8.209196251 1
 
0.1%
-5.325129738 1
 
0.1%
-74.3883679 1
 
0.1%
-71.86474638 1
 
0.1%
-60.87283276 1
 
0.1%
-62.42488238 1
 
0.1%
57.75325455 1
 
0.1%
68.72633149 1
 
0.1%
71.7807003 1
 
0.1%
83.56882019 1
 
0.1%
Other values (1398) 1398
99.3%
ValueCountFrequency (%)
-85.38046835 1
0.1%
-84.89110863 1
0.1%
-84.59901003 1
0.1%
-84.54862806 1
0.1%
-83.55804826 1
0.1%
-83.55562886 1
0.1%
-82.87129237 1
0.1%
-82.86128257 1
0.1%
-82.82419625 1
0.1%
-82.66146248 1
0.1%
ValueCountFrequency (%)
89.11847737 1
0.1%
87.49107515 1
0.1%
86.68064422 1
0.1%
85.34990518 1
0.1%
85.33815322 1
0.1%
85.27337778 1
0.1%
85.2189659 1
0.1%
85.17739266 1
0.1%
84.69099321 1
0.1%
84.50840672 1
0.1%

dataproduct_type
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size74.4 KiB
image
1408 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters7040
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowimage
2nd rowimage
3rd rowimage
4th rowimage
5th rowimage

Common Values

ValueCountFrequency (%)
image 1408
100.0%

Length

2025-05-25T15:34:07.500187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:07.570977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
image 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
i 1408
20.0%
m 1408
20.0%
a 1408
20.0%
g 1408
20.0%
e 1408
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1408
20.0%
m 1408
20.0%
a 1408
20.0%
g 1408
20.0%
e 1408
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1408
20.0%
m 1408
20.0%
a 1408
20.0%
g 1408
20.0%
e 1408
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1408
20.0%
m 1408
20.0%
a 1408
20.0%
g 1408
20.0%
e 1408
20.0%

proposal_pi
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size86.8 KiB
Ricker, George
1408 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters19712
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRicker, George
2nd rowRicker, George
3rd rowRicker, George
4th rowRicker, George
5th rowRicker, George

Common Values

ValueCountFrequency (%)
Ricker, George 1408
100.0%

Length

2025-05-25T15:34:07.659775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:07.739531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ricker 1408
50.0%
george 1408
50.0%

Most occurring characters

ValueCountFrequency (%)
e 4224
21.4%
r 2816
14.3%
R 1408
 
7.1%
c 1408
 
7.1%
i 1408
 
7.1%
k 1408
 
7.1%
, 1408
 
7.1%
1408
 
7.1%
G 1408
 
7.1%
o 1408
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4224
21.4%
r 2816
14.3%
R 1408
 
7.1%
c 1408
 
7.1%
i 1408
 
7.1%
k 1408
 
7.1%
, 1408
 
7.1%
1408
 
7.1%
G 1408
 
7.1%
o 1408
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4224
21.4%
r 2816
14.3%
R 1408
 
7.1%
c 1408
 
7.1%
i 1408
 
7.1%
k 1408
 
7.1%
, 1408
 
7.1%
1408
 
7.1%
G 1408
 
7.1%
o 1408
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4224
21.4%
r 2816
14.3%
R 1408
 
7.1%
c 1408
 
7.1%
i 1408
 
7.1%
k 1408
 
7.1%
, 1408
 
7.1%
1408
 
7.1%
G 1408
 
7.1%
o 1408
 
7.1%

calib_level
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.9 KiB
3
1408 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1408
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1408
100.0%

Length

2025-05-25T15:34:07.821347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:07.890162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
3 1408
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1408
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1408
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1408
100.0%

t_min
Real number (ℝ)

High correlation  Unique 

Distinct1408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59511.812
Minimum58324.812
Maximum60689.651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2025-05-25T15:34:07.975893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum58324.812
5-th percentile58437.482
Q158920.407
median59512.347
Q360104.414
95-th percentile60584.087
Maximum60689.651
Range2364.8394
Interquartile range (IQR)1184.0073

Descriptive statistics

Standard deviation691.57244
Coefficient of variation (CV)0.011620759
Kurtosis-1.2029703
Mean59511.812
Median Absolute Deviation (MAD)599.17802
Skewness-0.0037454877
Sum83792632
Variance478272.44
MonotonicityNot monotonic
2025-05-25T15:34:08.134494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60689.65025 1
 
0.1%
60689.65055 1
 
0.1%
60662.53349 1
 
0.1%
60662.53378 1
 
0.1%
60662.53497 1
 
0.1%
60662.53468 1
 
0.1%
59936.39636 1
 
0.1%
59936.39757 1
 
0.1%
59936.39784 1
 
0.1%
59936.39892 1
 
0.1%
Other values (1398) 1398
99.3%
ValueCountFrequency (%)
58324.81152 1
0.1%
58324.81181 1
0.1%
58324.81276 1
0.1%
58324.81304 1
0.1%
58324.81386 1
0.1%
58324.81414 1
0.1%
58324.81499 1
0.1%
58324.81527 1
0.1%
58324.81591 1
0.1%
58324.8162 1
0.1%
ValueCountFrequency (%)
60689.65096 1
0.1%
60689.65065 1
0.1%
60689.65055 1
0.1%
60689.65025 1
0.1%
60689.65 1
0.1%
60689.64969 1
0.1%
60689.64917 1
0.1%
60689.64886 1
0.1%
60689.64825 1
0.1%
60689.64795 1
0.1%

t_max
Real number (ℝ)

High correlation  Unique 

Distinct1408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59537.96
Minimum58352.666
Maximum60717.429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2025-05-25T15:34:08.284070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum58352.666
5-th percentile58463.773
Q158947.276
median59537.033
Q360132.921
95-th percentile60609.844
Maximum60717.429
Range2364.7631
Interquartile range (IQR)1185.645

Descriptive statistics

Standard deviation691.66283
Coefficient of variation (CV)0.011617174
Kurtosis-1.2038877
Mean59537.96
Median Absolute Deviation (MAD)599.97314
Skewness-0.0039652697
Sum83829447
Variance478397.46
MonotonicityNot monotonic
2025-05-25T15:34:08.444681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60717.42837 1
 
0.1%
60717.42807 1
 
0.1%
60689.43658 1
 
0.1%
60689.43629 1
 
0.1%
60689.43748 1
 
0.1%
60689.43778 1
 
0.1%
59962.08178 1
 
0.1%
59962.08299 1
 
0.1%
59962.08269 1
 
0.1%
59962.08377 1
 
0.1%
Other values (1398) 1398
99.3%
ValueCountFrequency (%)
58352.66567 1
0.1%
58352.66598 1
0.1%
58352.6669 1
0.1%
58352.66721 1
0.1%
58352.66799 1
0.1%
58352.6683 1
0.1%
58352.66912 1
0.1%
58352.66942 1
0.1%
58352.67004 1
0.1%
58352.67034 1
0.1%
ValueCountFrequency (%)
60717.42877 1
0.1%
60717.42847 1
0.1%
60717.42837 1
0.1%
60717.42807 1
0.1%
60717.42782 1
0.1%
60717.42752 1
0.1%
60717.42699 1
0.1%
60717.42669 1
0.1%
60717.42607 1
0.1%
60717.42577 1
0.1%

t_exptime
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean637.19972
Minimum158.39992
Maximum1425.5994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2025-05-25T15:34:08.586262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum158.39992
5-th percentile158.39992
Q1158.39993
median475.19979
Q31425.5994
95-th percentile1425.5994
Maximum1425.5994
Range1267.1995
Interquartile range (IQR)1267.1994

Descriptive statistics

Standard deviation527.6947
Coefficient of variation (CV)0.82814647
Kurtosis-1.2685631
Mean637.19972
Median Absolute Deviation (MAD)316.79986
Skewness0.69715956
Sum897177.21
Variance278461.7
MonotonicityNot monotonic
2025-05-25T15:34:08.742843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
158.399927 208
 
14.8%
158.399928 128
 
9.1%
475.19979 64
 
4.5%
158.399924 64
 
4.5%
475.199789 64
 
4.5%
475.199788 48
 
3.4%
158.399926 48
 
3.4%
475.199787 48
 
3.4%
1425.599392 32
 
2.3%
475.199791 32
 
2.3%
Other values (34) 672
47.7%
ValueCountFrequency (%)
158.399923 16
 
1.1%
158.399924 64
 
4.5%
158.399925 32
 
2.3%
158.399926 48
 
3.4%
158.399927 208
14.8%
158.399928 128
9.1%
158.399929 16
 
1.1%
158.39993 16
 
1.1%
475.199767 16
 
1.1%
475.199775 16
 
1.1%
ValueCountFrequency (%)
1425.599438 16
1.1%
1425.599428 16
1.1%
1425.599424 16
1.1%
1425.599419 16
1.1%
1425.599416 16
1.1%
1425.599414 16
1.1%
1425.59941 16
1.1%
1425.599406 32
2.3%
1425.599402 32
2.3%
1425.599399 16
1.1%

em_min
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size74.4 KiB
600.0
1408 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters7040
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row600.0
2nd row600.0
3rd row600.0
4th row600.0
5th row600.0

Common Values

ValueCountFrequency (%)
600.0 1408
100.0%

Length

2025-05-25T15:34:08.900423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:08.978213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
600.0 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
0 4224
60.0%
6 1408
 
20.0%
. 1408
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4224
60.0%
6 1408
 
20.0%
. 1408
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4224
60.0%
6 1408
 
20.0%
. 1408
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4224
60.0%
6 1408
 
20.0%
. 1408
 
20.0%

em_max
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.8 KiB
1000.0
1408 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8448
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000.0
2nd row1000.0
3rd row1000.0
4th row1000.0
5th row1000.0

Common Values

ValueCountFrequency (%)
1000.0 1408
100.0%

Length

2025-05-25T15:34:09.067973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:09.137787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1000.0 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5632
66.7%
1 1408
 
16.7%
. 1408
 
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5632
66.7%
1 1408
 
16.7%
. 1408
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5632
66.7%
1 1408
 
16.7%
. 1408
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5632
66.7%
1 1408
 
16.7%
. 1408
 
16.7%

obs_title
Unsupported

Missing  Rejected  Unsupported 

Missing1408
Missing (%)100.0%
Memory size11.1 KiB

t_obs_release
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59570.701
Minimum58458.583
Maximum60734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2025-05-25T15:34:09.231575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum58458.583
5-th percentile58514.667
Q158966
median59569
Q360171.75
95-th percentile60654
Maximum60734
Range2275.4167
Interquartile range (IQR)1205.75

Descriptive statistics

Standard deviation687.13477
Coefficient of variation (CV)0.011534777
Kurtosis-1.2178065
Mean59570.701
Median Absolute Deviation (MAD)604.5
Skewness0.027386367
Sum83875547
Variance472154.19
MonotonicityNot monotonic
2025-05-25T15:34:09.393130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58507.66667 32
 
2.3%
58458.58333 32
 
2.3%
58893 16
 
1.1%
58917 16
 
1.1%
60697 16
 
1.1%
59999 16
 
1.1%
60719 16
 
1.1%
59001 16
 
1.1%
58739.33333 16
 
1.1%
58756.33333 16
 
1.1%
Other values (76) 1216
86.4%
ValueCountFrequency (%)
58458.58333 32
2.3%
58507.66667 32
2.3%
58514.66667 16
1.1%
58541.83333 16
1.1%
58553.5 16
1.1%
58584.5 16
1.1%
58609.33333 16
1.1%
58635.33333 16
1.1%
58651.5 16
1.1%
58673.5 16
1.1%
ValueCountFrequency (%)
60734 16
1.1%
60719 16
1.1%
60697 16
1.1%
60662 16
1.1%
60654 16
1.1%
60635 16
1.1%
60613 16
1.1%
60573 16
1.1%
60545 16
1.1%
60515 16
1.1%

proposal_id
Unsupported

Missing  Rejected  Unsupported 

Missing1408
Missing (%)100.0%
Memory size11.1 KiB

proposal_type
Unsupported

Missing  Rejected  Unsupported 

Missing1408
Missing (%)100.0%
Memory size11.1 KiB

sequence_number
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.5
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2025-05-25T15:34:09.540735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q122.75
median44.5
Q366.25
95-th percentile84
Maximum88
Range87
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation25.410797
Coefficient of variation (CV)0.57102914
Kurtosis-1.2003098
Mean44.5
Median Absolute Deviation (MAD)22
Skewness0
Sum62656
Variance645.7086
MonotonicityNot monotonic
2025-05-25T15:34:09.695299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 16
 
1.1%
61 16
 
1.1%
20 16
 
1.1%
21 16
 
1.1%
22 16
 
1.1%
23 16
 
1.1%
87 16
 
1.1%
24 16
 
1.1%
14 16
 
1.1%
15 16
 
1.1%
Other values (78) 1248
88.6%
ValueCountFrequency (%)
1 16
1.1%
2 16
1.1%
3 16
1.1%
4 16
1.1%
5 16
1.1%
6 16
1.1%
7 16
1.1%
8 16
1.1%
9 16
1.1%
10 16
1.1%
ValueCountFrequency (%)
88 16
1.1%
87 16
1.1%
86 16
1.1%
85 16
1.1%
84 16
1.1%
83 16
1.1%
82 16
1.1%
81 16
1.1%
80 16
1.1%
79 16
1.1%

s_region
Text

Unique 

Distinct1408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size227.8 KiB
2025-05-25T15:34:09.935679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length137
Median length133
Mean length116.57315
Min length100

Characters and Unicode

Total characters164135
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1408 ?
Unique (%)100.0%

Sample

1st rowPOLYGON 84.397259 54.220049 84.340478 42.731886 67.849991 41.751568 64.359824 53.425924 84.397259 54.220049
2nd rowPOLYGON 64.081165 53.379423 67.62671 41.711452 52.195423 38.463737 46.379456 49.163669 64.081165 53.379423
3rd rowPOLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355
4th rowPOLYGON 70.29962 29.735295 67.909289 41.484483 84.326369 42.4735 83.925435 30.988125 70.29962 29.735295
5th rowPOLYGON 95.427814 77.267546 87.798089 66.012517 58.089341 65.068322 42.982691 75.947124 95.427814 77.267546
ValueCountFrequency (%)
polygon 1408
 
9.1%
27.410355 2
 
< 0.1%
39.07672300 2
 
< 0.1%
49.00371300 2
 
< 0.1%
13.10702100 2
 
< 0.1%
120.63726100 2
 
< 0.1%
66.43572200 2
 
< 0.1%
133.86981200 2
 
< 0.1%
77.76271200 2
 
< 0.1%
64.98799600 2
 
< 0.1%
Other values (11254) 14062
90.8%
2025-05-25T15:34:10.330598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16805
10.2%
14080
8.6%
. 14080
8.6%
2 13306
 
8.1%
1 13073
 
8.0%
3 12398
 
7.6%
6 11962
 
7.3%
5 11788
 
7.2%
7 11292
 
6.9%
4 11209
 
6.8%
Other values (9) 34142
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 164135
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16805
10.2%
14080
8.6%
. 14080
8.6%
2 13306
 
8.1%
1 13073
 
8.0%
3 12398
 
7.6%
6 11962
 
7.3%
5 11788
 
7.2%
7 11292
 
6.9%
4 11209
 
6.8%
Other values (9) 34142
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 164135
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16805
10.2%
14080
8.6%
. 14080
8.6%
2 13306
 
8.1%
1 13073
 
8.0%
3 12398
 
7.6%
6 11962
 
7.3%
5 11788
 
7.2%
7 11292
 
6.9%
4 11209
 
6.8%
Other values (9) 34142
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 164135
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16805
10.2%
14080
8.6%
. 14080
8.6%
2 13306
 
8.1%
1 13073
 
8.0%
3 12398
 
7.6%
6 11962
 
7.3%
5 11788
 
7.2%
7 11292
 
6.9%
4 11209
 
6.8%
Other values (9) 34142
20.8%

jpegURL
Unsupported

Missing  Rejected  Unsupported 

Missing1408
Missing (%)100.0%
Memory size11.1 KiB

dataURL
Unsupported

Missing  Rejected  Unsupported 

Missing1408
Missing (%)100.0%
Memory size11.1 KiB

dataRights
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.8 KiB
PUBLIC
1408 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8448
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPUBLIC
2nd rowPUBLIC
3rd rowPUBLIC
4th rowPUBLIC
5th rowPUBLIC

Common Values

ValueCountFrequency (%)
PUBLIC 1408
100.0%

Length

2025-05-25T15:34:10.444330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:10.507151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
public 1408
100.0%

Most occurring characters

ValueCountFrequency (%)
P 1408
16.7%
U 1408
16.7%
B 1408
16.7%
L 1408
16.7%
I 1408
16.7%
C 1408
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1408
16.7%
U 1408
16.7%
B 1408
16.7%
L 1408
16.7%
I 1408
16.7%
C 1408
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1408
16.7%
U 1408
16.7%
B 1408
16.7%
L 1408
16.7%
I 1408
16.7%
C 1408
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1408
16.7%
U 1408
16.7%
B 1408
16.7%
L 1408
16.7%
I 1408
16.7%
C 1408
16.7%

mtFlag
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
False
1408 
ValueCountFrequency (%)
False 1408
100.0%
2025-05-25T15:34:10.544052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

srcDen
Unsupported

Missing  Rejected  Unsupported 

Missing1408
Missing (%)100.0%
Memory size11.1 KiB

obsid
Real number (ℝ)

High correlation  Unique 

Distinct1408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0292041 × 108
Minimum27266910
Maximum2.4792257 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2025-05-25T15:34:10.642764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27266910
5-th percentile27428567
Q160805182
median70125368
Q31.690107 × 108
95-th percentile2.3359507 × 108
Maximum2.4792257 × 108
Range2.2065566 × 108
Interquartile range (IQR)1.0820552 × 108

Descriptive statistics

Standard deviation72530963
Coefficient of variation (CV)0.70472865
Kurtosis-0.91641484
Mean1.0292041 × 108
Median Absolute Deviation (MAD)42191594
Skewness0.74477472
Sum1.4491194 × 1011
Variance5.2607406 × 1015
MonotonicityNot monotonic
2025-05-25T15:34:10.793385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247535544 1
 
0.1%
247532686 1
 
0.1%
242083814 1
 
0.1%
242083813 1
 
0.1%
242083809 1
 
0.1%
242083780 1
 
0.1%
115340940 1
 
0.1%
115337898 1
 
0.1%
115335248 1
 
0.1%
115332163 1
 
0.1%
Other values (1398) 1398
99.3%
ValueCountFrequency (%)
27266910 1
0.1%
27266911 1
0.1%
27266912 1
0.1%
27266913 1
0.1%
27266914 1
0.1%
27266915 1
0.1%
27266916 1
0.1%
27266917 1
0.1%
27266918 1
0.1%
27266919 1
0.1%
ValueCountFrequency (%)
247922567 1
0.1%
247919459 1
0.1%
247916479 1
0.1%
247913721 1
0.1%
247910493 1
0.1%
247907669 1
0.1%
247904946 1
0.1%
247902682 1
0.1%
247900391 1
0.1%
247898034 1
0.1%

objID
Real number (ℝ)

High correlation  Unique 

Distinct1408
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2367928 × 108
Minimum70120107
Maximum7.3209552 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2025-05-25T15:34:10.931990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70120107
5-th percentile70441401
Q11.0897945 × 108
median1.3566978 × 108
Q32.9741574 × 108
95-th percentile6.7062452 × 108
Maximum7.3209552 × 108
Range6.6197541 × 108
Interquartile range (IQR)1.8843628 × 108

Descriptive statistics

Standard deviation1.8807625 × 108
Coefficient of variation (CV)0.84083002
Kurtosis0.86883784
Mean2.2367928 × 108
Median Absolute Deviation (MAD)64450513
Skewness1.4352409
Sum3.1494043 × 1011
Variance3.5372677 × 1016
MonotonicityNot monotonic
2025-05-25T15:34:11.092562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
731993104 1
 
0.1%
731986357 1
 
0.1%
717956010 1
 
0.1%
717953611 1
 
0.1%
717951275 1
 
0.1%
717945746 1
 
0.1%
213374889 1
 
0.1%
213371559 1
 
0.1%
213368570 1
 
0.1%
213365410 1
 
0.1%
Other values (1398) 1398
99.3%
ValueCountFrequency (%)
70120107 1
0.1%
70120108 1
0.1%
70120109 1
0.1%
70120110 1
0.1%
70120111 1
0.1%
70120112 1
0.1%
70120113 1
0.1%
70120114 1
0.1%
70120115 1
0.1%
70120116 1
0.1%
ValueCountFrequency (%)
732095520 1
0.1%
732085054 1
0.1%
732076216 1
0.1%
732067225 1
0.1%
732056593 1
0.1%
732049844 1
0.1%
732045432 1
0.1%
732038577 1
0.1%
732032390 1
0.1%
732028075 1
0.1%

Interactions

2025-05-25T15:34:02.656942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:54.247424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:55.322552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:56.442566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:57.477788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:58.531976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:59.635021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:00.697182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.656615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:02.800557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:54.370108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:55.453203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:56.561240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:57.610435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:58.662623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:59.742733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:00.845783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.767319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:02.941181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:54.507730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:55.648679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:56.670945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:57.723145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:58.780306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:59.848480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:00.953495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.877028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:03.071832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:54.616438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:55.774343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:56.773670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:57.824860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:58.905972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:59.949182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.055248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.972769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:03.188523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:54.737129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:55.897053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:56.872409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:57.930590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:59.032631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:00.043938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.166925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:02.074502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:03.322162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:54.868764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:56.001735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:57.002060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:58.049287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:59.155303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:00.272316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.269650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:02.174231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:03.423890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:54.976478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:56.108450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:57.112764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:58.160962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:59.287951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:00.365073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.366390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:02.272968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:03.542573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:55.082195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:56.219153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:57.212498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:58.287623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:59.390680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:00.458818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.457149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:02.397634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:03.670232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:55.192915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:56.327866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:57.318215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:58.416281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:33:59.512349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:00.570532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:01.552902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:02.493386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-25T15:34:11.375803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
objIDobsids_decs_rasequence_numbert_exptimet_maxt_mint_obs_release
objID1.0001.0000.0910.1060.881-0.8330.8810.8810.881
obsid1.0001.0000.0910.1060.881-0.8330.8810.8810.881
s_dec0.0910.0911.0000.3270.247-0.1730.2470.2470.247
s_ra0.1060.1060.3271.0000.169-0.1890.1700.1700.169
sequence_number0.8810.8810.2470.1691.000-0.9511.0001.0001.000
t_exptime-0.833-0.833-0.173-0.189-0.9511.000-0.951-0.951-0.951
t_max0.8810.8810.2470.1701.000-0.9511.0001.0001.000
t_min0.8810.8810.2470.1701.000-0.9511.0001.0001.000
t_obs_release0.8810.8810.2470.1691.000-0.9511.0001.0001.000

Missing values

2025-05-25T15:34:03.941549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-25T15:34:04.253671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

intentTypeobs_collectionprovenance_nameinstrument_nameprojectfilterswavelength_regiontarget_nametarget_classificationobs_ids_ras_decdataproduct_typeproposal_picalib_levelt_mint_maxt_exptimeem_minem_maxobs_titlet_obs_releaseproposal_idproposal_typesequence_numbers_regionjpegURLdataURLdataRightsmtFlagsrcDenobsidobjID
0scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-175.25737948.394182imageRicker, George360635.75911760662.331107158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 84.397259 54.220049 84.340478 42.731886 67.849991 41.751568 64.359824 53.425924 84.397259 54.220049NaNNaNPUBLICFalseNaN236870228700694124
1scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-257.52319146.001910imageRicker, George360635.75939860662.330808158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 64.081165 53.379423 67.62671 41.711452 52.195423 38.463737 46.379456 49.163669 64.081165 53.379423NaNNaNPUBLICFalseNaN236870229700703621
2scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.422100imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
3scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-476.59971736.405754imageRicker, George360635.75951360662.331509158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 70.29962 29.735295 67.909289 41.484483 84.326369 42.4735 83.925435 30.988125 70.29962 29.735295NaNNaNPUBLICFalseNaN236870243700721420
4scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-2-171.01834572.117377imageRicker, George360635.75775060662.329734158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 95.427814 77.267546 87.798089 66.012517 58.089341 65.068322 42.982691 75.947124 95.427814 77.267546NaNNaNPUBLICFalseNaN236870245700730556
5scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-2-237.50401067.492252imageRicker, George360635.75802660662.329431158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 42.356322 75.86303 57.677915 65.022354 34.630883 59.087104 16.286344 67.150322 42.356322 75.86303NaNNaNPUBLICFalseNaN236870248700740843
6scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-2-350.66781957.064990imageRicker, George360635.75884960662.330253158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 46.099441 49.386963 34.968903 58.878749 57.88955 64.757794 64.017263 53.26775 46.099441 49.386963NaNNaNPUBLICFalseNaN236870249700749778
7scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-2-473.56549460.128463imageRicker, George360635.75857360662.330557158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 64.289278 53.300712 58.27894 64.809113 87.684357 65.748281 84.289183 54.385971 64.289278 53.300712NaNNaNPUBLICFalseNaN236870333700758124
8scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-3-18.18414375.079718imageRicker, George360635.75711360662.328518158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 15.752091 67.29029 344.703486 70.125083 344.81612 82.345156 42.40894 75.855226 15.752091 67.29029NaNNaNPUBLICFalseNaN236870334700769279
9scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-3-261.19184483.924831imageRicker, George360635.75683260662.328817158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 42.971739 75.938329 344.850896 82.50859 164.325307 85.267937 95.996725 77.625463 42.971739 75.938329NaNNaNPUBLICFalseNaN236870335700778210
intentTypeobs_collectionprovenance_nameinstrument_nameprojectfilterswavelength_regiontarget_nametarget_classificationobs_ids_ras_decdataproduct_typeproposal_picalib_levelt_mint_maxt_exptimeem_minem_maxobs_titlet_obs_releaseproposal_idproposal_typesequence_numbers_regionjpegURLdataURLdataRightsmtFlagsrcDenobsidobjID
1398scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0088-3-1122.473158-43.113982imageRicker, George360689.64794760717.426073158.399925600.01000.0NaN60734.0NaNNaN88POLYGON 131.342739 -38.487043 129.884202 -49.926969 111.895425 -47.081574 116.904982 -35.774303 131.342739 -38.487043NaNNaNPUBLICFalseNaN247902682732038577
1399scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0088-3-2106.982355-39.295751imageRicker, George360689.64824960717.425770158.399925600.01000.0NaN60734.0NaNNaN88POLYGON 116.717635 -35.725121 111.674305 -47.021615 96.245686 -41.690473 103.348401 -31.701802 116.717635 -35.725121NaNNaNPUBLICFalseNaN247904946732045432
1400scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0060-4-4287.34868959.747791imageRicker, George359936.39663459962.081489158.399929600.01000.0NaN59982.0NaNNaN60POLYGON 277.098585 53.377399 273.176114 65.107147 302.236677 64.715346 296.987067 53.550094 277.098585 53.377399NaNNaNPUBLICFalseNaN115344219213378251
1401scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0085-4-4269.71534656.242872imageRicker, George360610.04772160635.542771158.399927600.01000.0NaN60662.0NaNNaN85POLYGON 254.789337 55.028168 268.844825 64.670956 284.765469 55.309459 270.259983 48.110875 254.789337 55.028168NaNNaNPUBLICFalseNaN233942808673578297
1402scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0088-3-399.457422-50.090478imageRicker, George360689.64714160717.424658158.399925600.01000.0NaN60734.0NaNNaN88POLYGON 86.750069 -51.486815 96.061656 -41.920416 111.536268 -47.281299 103.713821 -58.220326 86.750069 -51.486815NaNNaNPUBLICFalseNaN247907669732049844
1403scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0088-3-4118.085778-54.784110imageRicker, George360689.64684260717.424963158.399925600.01000.0NaN60734.0NaNNaN88POLYGON 103.995769 -58.278586 111.769394 -47.324364 129.847316 -50.175062 126.776921 -61.531426 103.995769 -58.278586NaNNaNPUBLICFalseNaN247910493732056593
1404scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0088-4-1110.714292-66.099514imageRicker, George360689.64577260717.423884158.399925600.01000.0NaN60734.0NaNNaN88POLYGON 126.593612 -61.814511 124.108074 -73.265737 89.536104 -68.254784 103.948456 -58.19583 126.593612 -61.814511NaNNaNPUBLICFalseNaN247913721732067225
1405scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0088-4-287.465791-59.730241imageRicker, George360689.64607760717.423585158.399925600.01000.0NaN60734.0NaNNaN88POLYGON 103.675924 -58.111101 89.215174 -68.141085 70.824184 -58.947013 86.440516 -51.602564 103.675924 -58.111101NaNNaNPUBLICFalseNaN247916479732076216
1406scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0088-4-366.291166-67.405962imageRicker, George360689.64488160717.422378158.399925600.01000.0NaN60734.0NaNNaN88POLYGON 48.343253 -63.919623 70.359333 -59.102819 88.689691 -68.369424 56.850403 -75.374341 48.343253 -63.919623NaNNaNPUBLICFalseNaN247919459732085054
1407scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0088-4-492.804009-76.843686imageRicker, George360689.64457760717.422679158.399925600.01000.0NaN60734.0NaNNaN88POLYGON 57.070484 -75.528303 89.037206 -68.474966 123.995198 -73.541803 107.433698 -84.701613 57.070484 -75.528303NaNNaNPUBLICFalseNaN247922567732095520